# English Text Understanding

DeepFloyd-IF is a pixel-based three-stage cascaded diffusion model that achieves unprecedented levels of photorealism and language understanding. Its efficiency surpasses current state-of-the-art models, achieving a zero-shot FID-30K score of 6.66 on the COCO dataset.

DistilBERT is a lightweight distilled version of BERT, retaining 97% of BERT's performance while being 40% smaller and 60% faster.

A fine-tuned version of the bert-base-uncased model on the wiki dataset, suitable for text-related tasks

DistilRoBERTa is a distilled version of the RoBERTa-base model with fewer parameters but faster speed, suitable for English text processing tasks.

ALBERT is a lightweight pre-trained language model based on the Transformer architecture, trained on English text through self-supervised learning with parameter-sharing features to reduce memory usage.

ALBERT is a lightweight BERT variant pre-trained on English corpora, reducing memory usage through parameter sharing, supporting masked language modeling and sentence order prediction tasks.

ALBERT XXLarge v1 is a Transformer model pretrained on English corpus using Masked Language Modeling (MLM) objective with parameter-sharing features.

A large English language model pre-trained with masked language modeling objectives, using improved BERT training methods

An English pre-trained model based on Transformer architecture, trained on massive text through masked language modeling objectives, supporting text feature extraction and downstream task fine-tuning